Is AI reasoning a learnable rule-based process?

Autonomous reasoning is the capacity for an artificial intelligence system to derive logical conclusions from premises through a structured, verifiable sequence of steps. Rather than relying solely on probabilistic pattern matching, reasoning is increasingly defined as a learnable, rule-based process that ensures consistency and accuracy in decision-making.

According to recent research (arXiv:2608.12325), the current lack of an operational definition for reasoning in generative AI prevents researchers from accurately measuring progress. By treating reasoning as a formal, rule-based system, developers can move away from "black box" outputs toward outcomes that are both reproducible and trustworthy.

Why is a formal definition of reasoning necessary for AI?

A formal definition of reasoning is necessary because without clear operational standards, it is impossible to distinguish between genuine logical deduction and statistical mimicry. When a system lacks defined rules, its reliability remains unverified, making it unsuitable for high-stakes environments like finance, healthcare, or legal analysis.